# This file is modified from https://github.com/traveller59/second.pytorch try: from collections.abc import Iterable except: from collections import Iterable import torch from torch import nn from torch._utils import _unflatten_dense_tensors from torch.nn.utils import parameters_to_vector bn_types = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d, nn.SyncBatchNorm) def split_bn_bias(layer_groups): "Split the layers in `layer_groups` into batchnorm (`bn_types`) and non-batchnorm groups." split_groups = [] for l in layer_groups: l1, l2 = [], [] for c in l.children(): if isinstance(c, bn_types): l2.append(c) else: l1.append(c) split_groups += [nn.Sequential(*l1), nn.Sequential(*l2)] return split_groups def get_master(layer_groups, flat_master: bool = False): "Return two lists, one for the model parameters in FP16 and one for the master parameters in FP32." split_groups = split_bn_bias(layer_groups) model_params = [[param for param in lg.parameters() if param.requires_grad] for lg in split_groups] if flat_master: master_params = [] for lg in model_params: if len(lg) != 0: mp = parameters_to_vector([param.data.float() for param in lg]) mp = torch.nn.Parameter(mp, requires_grad=True) if mp.grad is None: mp.grad = mp.new(*mp.size()) master_params.append([mp]) else: master_params.append([]) return model_params, master_params else: master_params = [[param.clone().float().detach() for param in lg] for lg in model_params] for mp in master_params: for param in mp: param.requires_grad = True return model_params, master_params def model_g2master_g(model_params, master_params, flat_master: bool = False) -> None: "Copy the `model_params` gradients to `master_params` for the optimizer step." if flat_master: for model_group, master_group in zip(model_params, master_params): if len(master_group) != 0: master_group[0].grad.data.copy_(parameters_to_vector([p.grad.data.float() for p in model_group])) else: for model_group, master_group in zip(model_params, master_params): for model, master in zip(model_group, master_group): if model.grad is not None: if master.grad is None: master.grad = master.data.new(*master.data.size()) master.grad.data.copy_(model.grad.data) else: master.grad = None def master2model(model_params, master_params, flat_master: bool = False) -> None: "Copy `master_params` to `model_params`." if flat_master: for model_group, master_group in zip(model_params, master_params): if len(model_group) != 0: for model, master in zip(model_group, _unflatten_dense_tensors(master_group[0].data, model_group)): model.data.copy_(master) else: for model_group, master_group in zip(model_params, master_params): for model, master in zip(model_group, master_group): model.data.copy_(master.data) def listify(p=None, q=None): "Make `p` listy and the same length as `q`." if p is None: p = [] elif isinstance(p, str): p = [p] elif not isinstance(p, Iterable): p = [p] n = q if type(q) == int else len(p) if q is None else len(q) if len(p) == 1: p = p * n assert len(p) == n, f'List len mismatch ({len(p)} vs {n})' return list(p) def trainable_params(m: nn.Module): "Return list of trainable params in `m`." res = filter(lambda p: p.requires_grad, m.parameters()) return res def is_tuple(x) -> bool: return isinstance(x, tuple) # copy from fastai. class OptimWrapper(): "Basic wrapper around `opt` to simplify hyper-parameters changes." def __init__(self, opt, wd, true_wd: bool = False, bn_wd: bool = True): self.opt, self.true_wd, self.bn_wd = opt, true_wd, bn_wd self.opt_keys = list(self.opt.param_groups[0].keys()) self.opt_keys.remove('params') self.read_defaults() self.wd = wd @classmethod def create(cls, opt_func, lr, layer_groups, **kwargs): "Create an `optim.Optimizer` from `opt_func` with `lr`. Set lr on `layer_groups`." split_groups = split_bn_bias(layer_groups) opt = opt_func([{'params': trainable_params(l), 'lr': 0} for l in split_groups]) opt = cls(opt, **kwargs) opt.lr, opt.opt_func = listify(lr, layer_groups), opt_func return opt def new(self, layer_groups): "Create a new `OptimWrapper` from `self` with another `layer_groups` but the same hyper-parameters." opt_func = getattr(self, 'opt_func', self.opt.__class__) split_groups = split_bn_bias(layer_groups) opt = opt_func([{'params': trainable_params(l), 'lr': 0} for l in split_groups]) return self.create(opt_func, self.lr, layer_groups, wd=self.wd, true_wd=self.true_wd, bn_wd=self.bn_wd) def __repr__(self) -> str: return f'OptimWrapper over {repr(self.opt)}.\nTrue weight decay: {self.true_wd}' # Pytorch optimizer methods def step(self) -> None: "Set weight decay and step optimizer." # weight decay outside of optimizer step (AdamW) if self.true_wd: for lr, wd, pg1, pg2 in zip(self._lr, self._wd, self.opt.param_groups[::2], self.opt.param_groups[1::2]): for p in pg1['params']: # When some parameters are fixed: Shaoshuai Shi if p.requires_grad is False: continue p.data.mul_(1 - wd * lr) if self.bn_wd: for p in pg2['params']: # When some parameters are fixed: Shaoshuai Shi if p.requires_grad is False: continue p.data.mul_(1 - wd * lr) self.set_val('weight_decay', listify(0, self._wd)) self.opt.step() def zero_grad(self) -> None: "Clear optimizer gradients." self.opt.zero_grad() # Passthrough to the inner opt. def __getattr__(self, k: str): return getattr(self.opt, k, None) def clear(self): "Reset the state of the inner optimizer." sd = self.state_dict() sd['state'] = {} self.load_state_dict(sd) # Hyperparameters as properties @property def lr(self) -> float: return self._lr[-1] @lr.setter def lr(self, val: float) -> None: self._lr = self.set_val('lr', listify(val, self._lr)) @property def mom(self) -> float: return self._mom[-1] @mom.setter def mom(self, val: float) -> None: if 'momentum' in self.opt_keys: self.set_val('momentum', listify(val, self._mom)) elif 'betas' in self.opt_keys: self.set_val('betas', (listify(val, self._mom), self._beta)) self._mom = listify(val, self._mom) @property def beta(self) -> float: return None if self._beta is None else self._beta[-1] @beta.setter def beta(self, val: float) -> None: "Set beta (or alpha as makes sense for given optimizer)." if val is None: return if 'betas' in self.opt_keys: self.set_val('betas', (self._mom, listify(val, self._beta))) elif 'alpha' in self.opt_keys: self.set_val('alpha', listify(val, self._beta)) self._beta = listify(val, self._beta) @property def wd(self) -> float: return self._wd[-1] @wd.setter def wd(self, val: float) -> None: "Set weight decay." if not self.true_wd: self.set_val('weight_decay', listify(val, self._wd), bn_groups=self.bn_wd) self._wd = listify(val, self._wd) # Helper functions def read_defaults(self) -> None: "Read the values inside the optimizer for the hyper-parameters." self._beta = None if 'lr' in self.opt_keys: self._lr = self.read_val('lr') if 'momentum' in self.opt_keys: self._mom = self.read_val('momentum') if 'alpha' in self.opt_keys: self._beta = self.read_val('alpha') if 'betas' in self.opt_keys: self._mom, self._beta = self.read_val('betas') if 'weight_decay' in self.opt_keys: self._wd = self.read_val('weight_decay') def set_val(self, key: str, val, bn_groups: bool = True): "Set `val` inside the optimizer dictionary at `key`." if is_tuple(val): val = [(v1, v2) for v1, v2 in zip(*val)] for v, pg1, pg2 in zip(val, self.opt.param_groups[::2], self.opt.param_groups[1::2]): pg1[key] = v if bn_groups: pg2[key] = v return val def read_val(self, key: str): "Read a hyperparameter `key` in the optimizer dictionary." val = [pg[key] for pg in self.opt.param_groups[::2]] if is_tuple(val[0]): val = [o[0] for o in val], [o[1] for o in val] return val class FastAIMixedOptim(OptimWrapper): @classmethod def create(cls, opt_func, lr, layer_groups, model, flat_master=False, loss_scale=512.0, **kwargs): "Create an `optim.Optimizer` from `opt_func` with `lr`. Set lr on `layer_groups`." opt = OptimWrapper.create(opt_func, lr, layer_groups, **kwargs) opt.model_params, opt.master_params = get_master(layer_groups, flat_master) opt.flat_master = flat_master opt.loss_scale = loss_scale opt.model = model # Changes the optimizer so that the optimization step is done in FP32. # opt = self.learn.opt mom, wd, beta = opt.mom, opt.wd, opt.beta lrs = [lr for lr in opt._lr for _ in range(2)] opt_params = [{'params': mp, 'lr': lr} for mp, lr in zip(opt.master_params, lrs)] opt.opt = opt_func(opt_params) opt.mom, opt.wd, opt.beta = mom, wd, beta return opt def step(self): model_g2master_g(self.model_params, self.master_params, self.flat_master) for group in self.master_params: for param in group: param.grad.div_(self.loss_scale) super(FastAIMixedOptim, self).step() self.model.zero_grad() # Update the params from master to model. master2model(self.model_params, self.master_params, self.flat_master)